We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement learning. The proof separates two stability mechanisms.
arXiv:2608. 27313v2 Announce Type: replace-cross Abstract: Quantile temporal-difference learning (QTD) is an effective method for learning return distributions through quantile approximation, yet its finite-time behavior remains poorly understood.
By Zijie Cheng, Xiang Li, Yang Peng, Zhihua Zhang
arXiv:2610. 13901v1 Announce Type: cross Abstract: We study the last iterate of standard asynchronous Q-learning from a single trajectory of a finite Markov decision process.
By Yang Peng
arXiv:2608. 12973v3 Announce Type: replace-cross Abstract: In this paper, we study online statistical inference for quantile-based distributional policy evaluation, which aims to characterize the distribution of discounted cumulative rewards under a given policy.
By Zijie Cheng, Yang Peng, Zhihua Zhang
arXiv:2302.07477v4 Announce Type: replace
Abstract: We study the optimal sample complexity of tabular reinforcement learning for infinite-horizon discounted Markov decision processes. The unrestricte...
By Shengbo Wang, Jose Blanchet, Peter Glynn
arXiv:2609.38880v1 Announce Type: new
Abstract: We study the last iterate of standard tabular temporal-difference (TD) learning from a single trajectory of a finite Markov reward process. For discoun...
By Yang Peng